Prying minds with mind-blowing optogenetics

Did you know there was such a thing as optogenetics? The idea alone completely blows my mind:

"Optogenetics uses light to control neurons which have been genetically sensitised to light. It is a neuromodulation technique employed in neuroscience that uses a combination of techniques from optics and genetics to control and monitor the activities of individual neurons in living tissue to precisely measure the effects of those manipulations in real-time. The key reagents used in optogenetics are light-sensitive proteins." [Wikipedia]

The "light sensitive proteins" mentioned above are a family of proteins, called opsins, that are found in the photoreceptor cells of the retina. These proteins are responsible for converting light (photons) into electrochemical signals.

So, in layman terms, the idea behind optogenetics is that if we can deliver these opsin proteins into the neurons, making them sensitive to light, we can then use light to control the neurons themselves. This is used to understand the function of certain cell types in the brain. How do you deliver the proteins to the neurons? Using viral vectors, of course. When injected into the brain, the viral vectors infect the neurons, delivering the opsin genes. These genes make the neurons sensitive to light and can therefore be activated or silenced using optical fibers delivering light. It sounds very much like science fiction, but basically this enables researchers to control neurons using optical fibers.

Source: Lumencor

This optic stimulation is limited to very small areas of the brain. Not only that. The way neurons react to light depends on the frequency used to stimulate them. Animal studies have shown that light stimulation of the ventral segmental area can induce depressive-like behaviors at 20 Hz, whereas increasing to 30 Hz (in a different study) elicited antidepressant effects.

Because there's a whole family of opsin proteins, current research is aimed at understanding which ones work best depending on the experimental setting and circumstances. For example, different opsins can elicit neurons at different wave lengths, and when there's no overlap between the two spectra, two different opsin proteins can be used simultaneously to obtain two different outcomes on neural activity. Pushing this even further, genes coding for these proteins can be mutated to change their wave-length and frequency sensitivity and can be optimized for certain experimental settings.

Researchers use optogenetics to identify brain circuits that control emotions like fear, depression, and anxiety, and all the areas involved in those circuits. Previous methods included local lesions, pharmacological treatment, and electrophysiological studies, but these didn't give complete control on the temporal window like light stimulation does, which can activate or inhibit neurons at a very precise moment. It's fascinating stuff that I confess I don't completely understand myself as it is not my field, so I welcome the input from any experts out there willing to share their view and any literature recommendations!

On a side note, CHIMERAS is now at $.99 for a limited time only! (Grab a copy if you love mysteries and science).

[1] Belzung C, Turiault M, & Griebel G (2014). Optogenetics to study the circuits of fear- and depression-like behaviors: A critical analysis. Pharmacology, biochemistry, and behavior, 122C, 144-157 PMID: 24727401

Autism: not one disease but a spectrum of disorders; not one gene but a network of gene coexpressions.

"Autism spectrum disorder (ASD) is a lifelong developmental condition that affects about 1 in 110 individuals, with onset before the age of three years. It is characterized by abnormalities in communication, impaired social function, repetitive behaviors and restricted interests [1]."

ASD is more common among males than females, with a 4:1 male to female ratio. Numerous studies in the literature have shown evidence for a strong genetic component of autism, with a risk up to 25 times higher among siblings compared to the general population. However, if you look at the literature, you find that these numbers change pretty dramatically from study to study. This is often the case when you look at rare disorders in conjunction with rare mutations (WARNING: the rest of the paragraph is a statistical digression, feel free to skip to the next section). The smaller the effect you are trying to measure, the more subjects you will need in your study. This is also true if you are testing many variants, as for example in GWAS studies, which investigate variants in the whole genome. If the effect is big enough, you will find statistical support for your association, however, if your sample size is not big enough, the effect you are trying to measure will vary greatly from study to study. This is because the smaller the sample size, the larger the variance, which is stat jargon to say that whatever you are trying to measure (typically an increase in risk) is likely to be different if you repeat the study.

What do we know about the genetic etiology of ASD? About 10% of people diagnosed with ASD have some underlying genetic syndrome (including mitochondrial genes). About 5% are due to rare chromosome rearrangements, for example changes in the size, shape, or number of some chromosomes. Another 5% has been associated to both inherited and de novo "copy number variations" (CNV), the presence of extra copies of some genes [1]. CNV is not rare among humans, as it accounts for approximately 0.4% of the variation between unrelated genomes. Identical twins also differ in CNV, and, even though they have identical genomes, the copy number of the genes may differ between the two. Despite this, in some families with a history of ASD the proportion of de novo CNV's has been found to be up to five times higher than in families without a history of ASD. Finally, thanks to recent advances in sequencing technology, de novo point mutations throughout hundreds of genes have been found and implicated in about 15% of ASD cases [2].

In light of the variety of mutations, genes, and phenotypes associated with ASD, two studies published in the last issue of Cell addressed the following question:

"do these genetic loci converge on specific biological processes, and where does the phenotypic specificity of ASD arise, given its genetic overlap with intellectual disability (ID)? [2]"

"if and when, in what brain regions, and in which cell types specific groups of ASD-related mutations converge during human brain development [3]" ?

Of the two papers, I've so far only read the one by Willsey et al. [3], who combined their own data with already published data and identified 144 de novo "loss-of-function (LoF)" mutations, in other words, mutations that impair the functionality of the gene (hence the corresponding protein is no longer produced). They called genes with 2 or more de novo LoF mutations "hcASD", or "high confidence" ASD because statistically they had a high probability of being truly associated with ASD. They also analyzed a less-likely set of genes with only one de novo LoF mutation, which they called "pASD genes".

Next, the researchers investigated when and where these genes are expressed during brain development. The way they did this is a bit technical, but to think about it in simple terms think of it this way: (1) they needed samples from brain tissues taken at different developmental stages; (2) they needed to look not just at one gene, but at families of genes that are likely to interact together and influence one another's likelihood of getting turned "on" and "off". When a gene is turned "on", the gene is coding a protein, and we say that the gene is "expressed."

To carry on their analysis, Willsey et al. used data published by Kang et al. (Nature, 2011) from "57 clinically unremarkable postmortem brains of diverse ancestry (31 males, 26 females) that span 15 consecutive periods of neurodevelopment and adulthood from 5.7 postconceptual weeks (PCW) to 82 years." The gene expression values were determined for each gene by brain region and by postmortem brain sample. Brain regions were grouped according to transcriptional similarity during fetal development. These data were used to generate 52 gene coexpression networks, each network composed of the hcASD genes and their top correlated genes. This coexpression network analysis is a technique that's been extensively used lately to analyze patterns of co-expressions of genes. Each gene in the network is represented by a node, and any two nodes (genes) at any given time are connected if the genes are expressed at that time.

Using this set-up, the researchers were able to link the ASD genes to particular brain regions and developmental phases.

"Our analysis identifies robust, statistically significant evidence for convergence of the input set of hcASD and pASD risk genes in glutamatergic projection neurons in layers 5 and 6 of human midfetal prefrontal and primary motor-somatosensory cortex (PFC-MSC). Given the extensive genetic and phenotypic heterogeneity underlying ASD and the small fraction of risk genes that we have examined in this study, this likely represents only one of several such points of convergence. Nonetheless, the analytic approach presented here clarifies key variables relevant for productive functional studies of specific ASD genes carrying LoF mutations, providing an important step in moving from gene discovery to an actionable understanding of ASD biology [3]."

Cortical glutamatergic projection neurons (CPNs) are a class of neocortical neurons. They are called "projection" neurons because they transmit information from the neocortex to other neocortical and central nervous system regions. During development, projection neurons are generated in the neocortical germinal zone and migrate radially to their final neocortical position. In their study, Wyllsey et al found that the development of midfetal CPNs is particularly vulnerable to ASD. Furthermore, the set of ASD genes they identified as associated to ASD are functionally diverse and encode proteins found in distinct cell compartments, confirming the theory that ASD can be caused by different and distinct pathways.

"Given recent studies suggesting that as many as 1,000 genes or more could contribute to ASD (He et al., 2013; Iossifov et al., 2012; Sanders et al., 2012), our analysis has uncovered a surprising degree of developmental convergence. Despite starting with only nine hcASD seed genes, we have identified highly significant and robust evidence for the contribution of coexpression networks relevant to L5 and L6 CPNs in two overlapping periods of midfetal human development (3–5 and 4–6) corresponding to 10–24 PCW [3]."

The importance of these studies lies in the understanding of not just the genetic association per se, but in the mechanisms that drive these associations, and, most importantly, how the numerous genes interact and when.

[1] Devlin and Schrer (2012). Genetic architecture in autism spectrum disorder Genetics & Development DOI: 10.1016/j.gde.2012.03.002

[2] Neelroop N. Parikshak, Rui Luo, Alice Zhang, Hyejung Won, Jennifer K. Lowe, Vijayendran Chandran, Steve Horvath, Daniel H. Geschwind (2013). Integrative Functional Genomic Analyses Implicate Specific Molecular Pathways and Circuits in Autism Cell DOI: 10.1016/j.cell.2013.10.031

[3] A. Jeremy Willsey, Stephan J. Sanders, Mingfeng Li, Shan Dong, Andrew T. Tebbenkamp, Rebecca A. Muhle, Steven K. Reilly, Leon Lin, Sofia Fertuzinhos, Jeremy A. Miller, Michael T. Murtha, Candace Bichsel, Wei Niu, Justin Cotney, A. Gulhan Ercan-Sencicek, J (2013). Coexpression Networks Implicate Human Midfetal Deep Cortical Projection Neurons in the Pathogenesis of Autism Cell DOI: 10.1016/j.cell.2013.10.020

Hail to the Bonobos!

Is human nature prone to violence or is cooperation the dominant trait? The latest issue of Science magazine is dedicated to "Human Conflict" and touches a variety of topics, from racism to terrorism, addressing the question: are we good or evil? Is our true nature aggressive and violent, but tamed by social constraints, or is it the other way around, and we are in fact neutrally inclined towards empathy and cooperation, while violence and aggression are the exceptions?

Concepts like "survival of the fittest" and the "selfishness of genes" (a concept I truly dislike, I'm just quoting it here because it seems to be a widespread view) have reinforced the general idea that our true nature is geared towards conflict. Life is conquered through competition.

In [1], de Waal argues that while after World War II the dominant view was that human nature was dominantly aggressive, there isn't much evidence to favor this view.

"During most of our prehistory, we were nomadic hunter-gatherers, whose cultures are nowadays not particularly known for warfare. They do occasionally raid, ambush, and kill their neighbors, but more often trade with them, intermarry, and permit travel through their territories. Hunter-gatherers illustrate a robust potential for peace and cooperation."

Some of our ancestors, like the chimpanzee Pan troglodytes, can be very aggressive and their territorial encounters are often lethal. However, among all chimpanzees, guess who's our closest relative? The Bonobo! if you are not familiar with this wonderful animal, check out what the Wikipedia page says:

"The bonobo is popularly known for its high levels of sexual behavior. Sex functions in conflict appeasement, affection, social status, excitement, and stress reduction. It occurs in virtually all partner combinations and in a variety of positions. This is a factor in the lower levels of aggression seen in the bonobo when compared to the common chimpanzee and other apes. Bonobos are perceived to be matriarchal; females tend to collectively dominate males by forming alliances and use sexuality to control males. A male's rank in the social hierarchy is often determined by his mother's rank."

Such behaviors force us to review the original theory that conflict is the driving force of survival. If that were truly the case, why do some chimpanzee show reconciliatory behaviors such as kissing and hugging after a fight? Furthermore, de Waal argues, all functions that are necessary for survival, such as sex, eating, nursing, and socializing, are associated with a sense of fulfillment. This is not the case with killing and aggressiveness.

Not only do bonobos show signs of empathy in their behavior (such as comforting one another after a disturbance), but also in the anatomy of their brains:

"This species has more gray matter in brain regions involved in the perception of distress, including the right dorsal amygdala and right anterior insula, and a better developed circuitry for inhibiting aggression."

Though highly speculative, evidence of empathy comes also from the fact that monkeys, like humans, have "mirror neurons," neurons that fire when a stimulus is experienced as well as when when, instead, it's observed. Since empathy involves embracing another individual's feelings, mirror neurons are often considered a sign of empathy. A wide range of empathy-based behaviors have been observed in primates, mice, and elephants, from mice discarding food in order to help a trapped companion, to incentive-free assistance in apes. And finally, contrary to aggression and violence, altruism is often followed by a sense of fulfillment.

EDIT: I really appreciate the discussion that this particular post sparked. I just want to add one more thought. I think the general conception has always been that aggression, lethal confrontations, and competition for resources are intrinsic to primitive animal behaviors. On the other hand, we think of sentiments like love, compassion, and empathy in particular as "higher" sentiments, something that pertains to civilization and hence to higher intelligence. This particular paper intrigues me because, without proving anything, it provides evidence to the opposite: it seems to indicate that empathy pertains to all animals, from mice to apes, not just to humans, and that evolution favors cooperation, while disfavoring disruptive confrontations. It is true that with limited resources aggressive behaviors increase, but if you take a society like the bonobos and observe it when in equilibrium with its own environment, then one can't help but wonder: if the bonobos manage to resolve their issue peacefully, why can't we do the same? It seems to me that while empathy is shared across the animal kingdom, envy, jealousy and greed are uniquely human. That certainly puts the phrase "higher intelligence" in perspective.

[1] de Waal, F. (2012). The Antiquity of Empathy Science, 336 (6083), 874-876 DOI: 10.1126/science.1220999

Young or old it doesn't matter: we need them both

To honor Brain Awareness Week I thought I'd try and discuss a neuroscience paper this week. It's not my field, so you'll have to be patient with me (and you experts out there are more than welcome to pitch in). I found a really fascinating story in the latest issue of Science [1] on the differences in information processing between "young" and "old" neurons. In order to understand the story, I had to take a couple of steps back and review a few things about the brain.

The hippocampus is the part of the brain that's responsible for learning, storing memories and associating them with feelings and emotions. Within the hippocampus lies the dentate gyrus, which is where adult neurogenesis takes place -- the formation of new neurons throughout adulthood. The middle layer of the dentate gyrus contains a type of neurons called granule cells. These are constantly generated and take a few weeks to develop and integrate in the dentate gyrus network. In [1], Marin-Burgin et al. asked the following question:

"Is it solely the continuous addition of new neurons to the network that is important, or are there specific functional properties only attributable to new granule cells (GCs) that are relevant to information processing?"

In order to answer the question, the researchers compared immature granule cells to mature ones in mouse hippocampus. The part that fascinates me the most about these experiments is that in order to "see" the different cells, these neurons are "retrovirally labeled to express red fluorescent protein." What this means is that a genetically engineered retrovirus that preferentially infects this type of cells is used to "infect" them and deliver the fluorescent proteins so that the neural activity can be visualized. Pretty cool, right?

Marin-Burgin et al. found that the dentate gyrus is made of a heterogeneous population of granule cells of different ages and that the different subpopulations have distinct activation thresholds. When given both excitatory and inhibitory input, the ratio of excitation to inhibition favors inhibition in mature granule cells, whereas immature cells have fewer inhibitory inputs (hehe, sounds familiar don't you think?). In other words, younger cells respond more easily and broadly, whereas older cells tend to be more specific. The fact that both are present at all times suggests that this range in different responses is needed for the correct functionality of the dentate gyrus, in particular for the correct storing and integration of novel information.

[1] Marin-Burgin, A., Mongiat, L., Pardi, M., & Schinder, A. (2012). Unique Processing During a Period of High Excitation/Inhibition Balance in Adult-Born Neurons Science, 335 (6073), 1238-1242 DOI: 10.1126/science.1214956

I know that face! Sort of...

In graduate school I had a Chinese friend who one day asked me the name of the fellow student who'd just stopped by to borrow a book. I told her, she thanked me, and added, "It's so hard for me to remember faces. You guys look all alike to me."

Now, you have to understand that I'm petite, brunette with dark eyes (very Italian), and the girl she'd just asked about was the typical Northern European type, tall, blond, and blue eyes. The concept was truly intriguing. I tend to mix up Eastern Asians, but that day I learned that Asians tend to mix up Caucasians.

You may have noticed this in other contexts, for example when people tell you who you or your child looks like and they come up with the funniest things. However, the "other-race" effect (less accurate recognition of people of a different race than self) is real and has been documented in the literature [1,2]. In these studies, participants were presented faces from different ethnic groups, including their own. In a second phase, a mix of already observed and never-seen before faces was presented, and participants had to recognize which they had already seen. In [1], researchers measured different brain potentials (through EEG) and inferred a pattern between the potential intensities with the act of remembering a face:

"Individuation may tend to be uniformly high for same-race faces but lower and less reliable for other-race faces. Individuation may also be more readily applied for other-race faces that appear less stereotypical. These electrophysiological measures thus provide novel evidence that poorer memory for other-race faces stems from encoding that is inadequate because it fails to emphasize individuating information."

An event-related potential (or ERP) is a brain response to a stimulus (the faces, in this particular case). They are measured through EEG and they have several components as shown in the figure below (P1, N1, P2, N2, and P2):

In [1] researchers found interesting patterns between two components in particular, P2 and N200, and the ability to recognize a face:

"Among all the potentials examined, frontocentral N200 potentials and occipitotemporal P2 potentials were particularly informative because they yielded other-race-specific memory findings. We thus propose that these potentials indexed face individuation that tended to be uniformly high for same-race faces but lower and more variable for other-race faces."

Even though I don't have the expertise to understand all the technicalities presented in the paper (but I do welcome comments, if anybody out there wants to provide more insight), I find these results quite intriguing. For example, participants were also asked to rate the "racial typicality" of each face, and other-race faces that were "less typical" seemed to be easier to recognize. Also, the amount of exposure of an individual to the other-race group affects the results, as the brain can indeed train itself to recognition.

A curious trivia is that both studies state at the beginning that all participants were right-handed. Is there a reason for this? Does being left-handed introduce a bias in this kind of studies?

[1] Lucas, H., Chiao, J., & Paller, K. (2011). Why Some Faces won't be Remembered: Brain Potentials Illuminate Successful Versus Unsuccessful Encoding for Same-Race and Other-Race Faces Frontiers in Human Neuroscience, 5 DOI: 10.3389/fnhum.2011.00020

[2] Herzmann, G., Willenbockel, V., Tanaka, J., & Curran, T. (2011). The neural correlates of memory encoding and recognition for own-race and other-race faces Neuropsychologia, 49 (11), 3103-3115 DOI: 10.1016/j.neuropsychologia.2011.07.019

Of hierarchies, mice, and neurons

It's shared across very different species, from ants and bees all the way up to chimpanzees and humans: social hierarchy dictates the structure of a group, and the ability to correctly recognize an individual's status, as well as their own, is crucial to successful interactions in the group.

Interestingly, social cognition is distinct from social status recognition, as demonstrated by studies on humans with brain lesions [1]. Neuroimaging also revealed that social status recognition has its own distinct network of brain regions, which includes the inferior parietal lobe (IPL), dorsolateral and ventrolateral prefrontal cortices (DLPFC and VLPFC), and portions of occipitotemporal lobe (OG). Social status is recognized through a range of nonverbal clues. For example, primates and humans are sensitive to facial expressions (such as direct eye contact) and body postures that make an individual "look" larger or more imposing.

These cues are processed through the DLPFC and VLPFC regions, which are usually associated with socioemotional responses and behavioral inhibition. They can overrule automatic responses in situations where the dominant individual imposes compliance to social norms.

As Chiao concludes in [1]:

"Given the ubiquitous presence of social hierarchy across species and cultures, an outstanding question in social neuroscience is to understand how adaptive mechanisms in the mind and brain support the production and maintenance of social hierarchy. Recent social neuroscience studies show that distinct neural systems are involved in the recognition and experience of social hierarchy, and that activity within these brain regions are modulated by individual and cultural factors."

A recent study published in Science [2] found a correlation between synaptic strength (the signals between neurons) and social rank. The researchers used a mouse model to investigate potential differences in the synaptic properties in the medial PFC region (which is the homologue equivalent of the human dorsolateral and medial PFC regions) between dominant and subordinate mice. They used the test tube to rank the social hierarchy among cage groups of 4 mice each: the tube only lets one mouse through and the challenge is to push the opponent out of the tube.

Researchers found that dominant mice have larger synaptic strength than the subordinate ones. Neurons transmit signals through chemicals called neurotransmitters, which are stored in vesicles and released at the synapse (the structure that transfers chemical signals between neighboring neurons). The strength of a signal can be measured in terms of "quantal release," which basically measures the number of effective vesicles released in response to an impulse. Wang et al. detected a higher quantal release in dominant mice. Furthermore, they proved that the opposite is also true: lowering the strength of these signals caused mice to lower in social rank.

In order to prove this, they manipulated the synaptic transmission mediated by a receptor called AMPA. They delivered DNA to the mouse brain with a viral vector that preferentially infects pyramidal neurons. Using this mechanism, Wang et al. were able to either amplify or deplete the amplitudes of AMPA-mediated synaptic currents, and when they did so they noticed that mice with stronger synaptic signals moved up in the social hierarchy, whereas the ones with lower signals moved downwards in ranking.

In the Perspective review accompanying the paper [3], Maroteaux and Mameli conclude:

"Wang et al. provide two conceptual advances: the idea that a neurobiological substrate for social ranking is located in the mPFC, and that synaptic efficacy represents a cellular substrate determining social status. Although the mPFC has an established role in social behavior, it cannot be considered the only structure where dominance is encoded. Future studies will be necessary to determine the hierarchical organization among brain structures underlying this complex behavior."

[1] Chiao, J. (2010). Neural basis of social status hierarchy across species Current Opinion in Neurobiology, 20 (6), 803-809 DOI: 10.1016/j.conb.2010.08.006

[2] Wang, F., Zhu, J., Zhu, H., Zhang, Q., Lin, Z., & Hu, H. (2011). Bidirectional Control of Social Hierarchy by Synaptic Efficacy in Medial Prefrontal Cortex Science, 334 (6056), 693-697 DOI: 10.1126/science.1209951

[3] Maroteaux, M., & Mameli, M. (2011). Synaptic Switch and Social Status Science, 334 (6056), 608-609 DOI: 10.1126/science.1214713

Learning neuroscience from a virus

Back in college, the shortest theorem proof I sat through in class (I was a math major) was the following: "Suppose the topological manifold is a chunk of cheese. Put a mouse on one of the cells and wait until the mouse has eaten all of the cheese." The cells, in that context, weren't biological cells, but rather topological ones. Believe me, it was a real proof and, once you worked out the details, it held.

So now suppose that instead of cheese you have a brain, and instead of topological cells you have neurons. What would the mouse be?

The nervous system processes information through a network across neurons. Neurons communicate exchanging signals (either chemical or electrical) through synapses. These network exchanges across neurons can be reconstructed with the use of chemical tracers, which allow researchers to visualize the activity of a specific neuron with its neighbors. However, chemical tracers have limits: not all of them can trace the "output" signal from a neuron, and not all of them are able to cross synapses. A study recently published in PNAS [1] presents a new way to trace neural circuits, using... guess what? Yes, you've guessed it: a virus. Pretend the brain is a chunk of cheese and let the virus "eat it all up." Well, okay, in principle.

With the disclaimer that neuroscience is not my field (but I'm always fascinated by any creative use of viruses), let me give you my two-cent-worth understanding of what was done. 

The authors genetically modified VSV, the vesicular stomatitis virus, enabling it to travel back and forth across synapses.  They placed a fluorescent reporter upstream of the viral proteins, and then they injected it into a mouse model. The virus used the communication network established by the neurons to infect the brain tissue. Basically, the path of infection followed the neural network in the mouse brain. Pretty cool how these pesky little viruses come in handy!

[1] Beier, K., Saunders, A., Oldenburg, I., Miyamichi, K., Akhtar, N., Luo, L., Whelan, S., Sabatini, B., & Cepko, C. (2011). From the Cover: Anterograde or retrograde transsynaptic labeling of CNS neurons with vesicular stomatitis virus vectors Proceedings of the National Academy of Sciences, 108 (37), 15414-15419 DOI: 10.1073/pnas.1110854108